Investigating the predictions of a memory-based account of statistical learning

Sandrine Girard, Carnegie Mellon University

Erik Thiessen, Carnegie Mellon University

Abstract

Statistical learning (SL) refers to the ability to extract
statistical regularities from the environment. Many researchers believe that SL
arises as a consequence of the way that information is stored and accessed in
memory (Thiessen, Kronstein, & Hufnagle, 2013). Accordingly, manipulations that
influence memory should have similar effects in SL experiments. In the current
study, participants were presented with artificial languages that varied along
two dimensions known to impact memory: number of distractors in the input and
timing of presentation (e.g., spaced vs. massed). Participants' performance was
clearly influenced by these manipulations; for example, the ability to segment a
word (e.g., "dupona") differed as a function of whether there was one frequent
competitor (e.g., "dugalo") or several less frequent competitors (e.g., "dugalo,"
"dufalu," "dumiso"). Experimental results were compared to two memory-based
computational models of SL (PARSER and TRACX). Implications of the experimental
results and model comparisons will be discussed.